Generate a structured data profile for a specified database table.
npx skills add https://github.com/miptah21/skills --skill profiling-tables-miptah21
Please help me install this Agent Skill. Skill: profiling-tables Source: https://github.com/miptah21/skills/tree/main/.agents/skills/profiling-tables Command: npx skills add https://github.com/miptah21/skills --skill profiling-tables-miptah21
It helps you quickly understand what data a specific table contains, how large it is, what each column looks like, and whether there are obvious quality issues.
Example: If you inherit an unknown analytics table, you can use this Skill to produce a ready-to-share profile with key statistics, a data quality score, and a short list of recommended follow-up queries.
Ask: “Profile the table <schema>.<table> and produce a structured data profile including schema, statistics, cardinality, sample rows, and a data quality score.”
High-intent search queries and answers about installing and using this skill.
Table profiling computes row counts, column statistics, cardinality skews, and representative samples to explain a database table's structure, contents, and quality signals. It resolves schema details via INFORMATION_SCHEMA and summarizes completeness, uniqueness, and validity concerns to surface risks early.
Data quality assessment summarizes completeness, uniqueness signals, freshness, validity concerns, and consistency checks to surface risks early. It computes row counts and column-wise metrics by data type to produce a structured profile with a data quality score for the inherited table.
Cardinality analysis and column statistics are computed by pulling column names, data types, and comments via INFORMATION_SCHEMA. The profiling process evaluates column statistics by data type, identifies cardinality skews, and extracts representative sample rows for the specified table.
Data profiling applies to dataset onboarding, dataset understanding, and troubleshooting when you need statistics for a concrete table name. It generates a ready-to-share profile with key statistics, a data quality score, and recommended follow-up queries to help new teammates interpret the dataset correctly.
Table profiling requires no additional dependencies or external components to function. It relies on standard SQL diagnostics via INFORMATION_SCHEMA to resolve table and schema details, compute row-count and column-wise metrics by data type, and output the structured data profile.
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